Description

Figure 1 is going to be two panes. The first pane (Fig. 1a) will have total publications published over time. The second pane (Fig. 1b) will have three lines (ecology, governance, and socioeconomics) and will show the frequency of these types of publicatons over time. All three categories in pane 2 (Fig. 1b) will not add up to the total publications in pane 1 (Fig. 1a).

Reading in Data

Data contains ‘orphans’.

mydata <- read_csv(here("data", "Data_total_working_for_r.csv"))

Pane 1 Wrangling

Subset to only look at year and types of co-location.

data_sub <- mydata %>% 
  select(first_author, year, lme, ecology, governance, socioecon) %>% 
  group_by(year) %>% 
  summarize(count = n())

Pane 1 Viz

# Pane 1 (Fig. 1a)
# count of publications over time

g1 <- ggplot(data = data_sub, aes(x = year, y = count)) +
  geom_line(color = "darkgray", size = .7) +
  labs(x = "Year",
       y = "Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45))

ggplotly(g1)

Pane 2 Wrangling

This might be semi sloppy code and there might be a cleaner way to do this but I’m not sure what that would be and I feel confident in the messy coding I’ve done here.

# Need to rearrange the data for pane 2
# Plan to create a subset that filters for all the ecology counts and totals them over time
  # then do the same thing for governance and socioecon
  # then rbind them together

data_sub_b_ecol <- mydata %>% 
  select(first_author, year, lme, ecology) %>% 
  group_by(year) %>% 
  summarize(count = sum(ecology))

ecology <- rep(c("Ecology"), times = 12)

#colnames <- c("year", "discipline", "count")

ecol_sub <- cbind(data_sub_b_ecol$year,
              ecology,
              data_sub_b_ecol$count)

ecol_df <- data.frame(ecol_sub) %>% 
  rename(year = V1) %>% 
  rename(discipline = ecology) %>% 
  rename(count = V3)
# same thing for socioeconomics section
data_sub_b_socio <- mydata %>% 
  select(first_author, year, lme, socioecon) %>% 
  group_by(year) %>% 
  summarize(count = sum(socioecon))

socioecon <- rep(c("Socioeconomics"), times = 12)

#can use the colnames value from above

socio_sub <- cbind(data_sub_b_socio$year,
              socioecon,
              data_sub_b_socio$count)

socio_df <- data.frame(socio_sub) %>% 
  rename(year = V1) %>% 
  rename(discipline = socioecon) %>% 
  rename(count = V3)
# same thing for governance section
data_sub_b_gov <- mydata %>% 
  select(first_author, year, lme, governance) %>% 
  group_by(year) %>% 
  summarize(count = sum(governance))

governance <- rep(c("Governance"), times = 12)

#can use the colnames value from above

gov_sub <- cbind(data_sub_b_gov$year,
              governance,
              data_sub_b_gov$count)

gov_df <- data.frame(gov_sub) %>% 
  rename(year = V1) %>% 
  rename(discipline = governance) %>% 
  rename(count = V3)
# rbinding the 3 dfs into one

all_df <- rbind(ecol_df, gov_df, socio_df) %>% 
  mutate(year = as.numeric(year)) %>% 
  mutate(count = as.integer(count))

# class was checked and its a data frame

Pane 2 Viz

Using ggplotly() here to allow us to look at each value, when needed.

g2 <- ggplot(data = all_df, aes(x = year, y = count)) +
  geom_line(aes(color = discipline), size = .7) +
  labs(x = "Year",
       y = "Count") +
  theme_minimal() +
  theme(legend.title = element_blank(), legend.position= 'bottom', axis.text.x = element_text(angle = 45)) +
  scale_colour_manual(values = c("Ecology" = "#00BA38", "Governance" = "#619CFF", "Socioeconomics" = "#F8766D"))

ggplotly(g2)

Patching panes together

Using cowplot to patch panes 1 and 2 together

plot_grid(g1, g2, labels = c("A", "B"))

Figure 1. Shows frequency of publications over time (years 2000-2020). Pane A reflects the total frequency of co-location publications published over time. Pane B reflects the frequency of co-location publications, separated by publication discipline (ie ecology, governance or socioeconomics), published over time.